---
title: Google
description: Learn how to use Google's Generative AI SDK with the Vercel AI SDK.
---

# Google

<Note type="warning">
  The legacy Google integration is not compatible with the AI SDK 3.1 functions.
  It is recommended to use the [AI SDK Google Generative AI
  Provider](../ai-sdk-providers/google-generative-ai) or the [AI SDK Google
  Vertex Provider](../ai-sdk-providers/google-vertex) instead.
</Note>

Vercel AI SDK provides a set of utilities to make it easy to use [Google's Generative AI SDK](https://github.com/google/generative-ai-js)
that enables you to build apps using [Google Gemini](https://ai.google.dev/).
In this guide, we'll walk through how to use the utilities to create a chat bot and a text completion app.

## Guide: Chat Bot

<Steps>

### Create a Next.js app

Create a Next.js application and install `ai`:

```shell
pnpm dlx create-next-app my-ai-app
cd my-ai-app
pnpm install ai @google/generative-ai
```

### Add your Google API Key to `.env`

Create a `.env` file in your project root and add your [API Key for the Google AI SDK](https://makersuite.google.com/app/apikey):

```shell file=".env"
GOOGLE_API_KEY=xxxxxxx
```

### Create a Route Handler

Create a Next.js Route Handler that generates a response to a series of messages via Google's Generative AI SDK, and returns the response as a streaming text response.

For this example, we'll create a route handler at `app/api/chat/route.ts` that accepts a `POST` request with a `messages` array of strings:

```tsx file="app/api/chat/route.ts" showLineNumbers
import { GoogleGenerativeAI } from '@google/generative-ai';
import { GoogleGenerativeAIStream, Message, StreamingTextResponse } from 'ai';

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY || '');

// convert messages from the Vercel AI SDK Format to the format
// that is expected by the Google GenAI SDK
const buildGoogleGenAIPrompt = (messages: Message[]) => ({
  contents: messages
    .filter(message => message.role === 'user' || message.role === 'assistant')
    .map(message => ({
      role: message.role === 'user' ? 'user' : 'model',
      parts: [{ text: message.content }],
    })),
});

export async function POST(req: Request) {
  // Extract the `prompt` from the body of the request
  const { messages } = await req.json();

  const geminiStream = await genAI
    .getGenerativeModel({ model: 'gemini-pro' })
    .generateContentStream(buildGoogleGenAIPrompt(messages));

  // Convert the response into a friendly text-stream
  const stream = GoogleGenerativeAIStream(geminiStream);

  // Respond with the stream
  return new StreamingTextResponse(stream);
}
```

<Note>
  Vercel AI SDK provides 2 utility helpers to make the above seamless: First, we
  pass the streaming `response` we receive from Google's Generative AI SDK to
  [`GoogleGenerativeAIStream`](/docs/reference/stream-helpers/google-generative-ai-stream).
  This utility class decodes/extracts the text tokens in the response and then
  re-encodes them properly for simple consumption. We can then pass that new
  stream directly to
  [`StreamingTextResponse`](/docs/reference/stream-helpers/streaming-text-response).
  This is another utility class that extends the normal Node/Edge Runtime
  `Response` class with the default headers you probably want (hint:
  `'Content-Type': 'text/plain; charset=utf-8'` is already set for you).
</Note>

### Wire up the UI

Create a Client component with a form that we'll use to gather the prompt from the user and then stream back the completion from.
By default, the [`useChat`](/docs/reference/ai-sdk-ui/use-chat) hook will use the `POST` Route Handler we created above (it defaults to `/api/chat`). You can override this by passing a `api` prop to `useChat({ api: '...'})`.

```tsx file="app/page.tsx" showLineNumbers
'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div>
      {messages.map(m => (
        <div key={m.id}>
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}
```

</Steps>

## Guide: Text Completion

<Steps>

### Use the Completion API

Similar to the Chat Bot example above, we'll create a Next.js Route Handler that generates a text completion via the same
Google Generative AI SDK that we'll then stream back to our Next.js. It accepts a `POST` request with a `prompt` string:

```tsx file="app/api/completion/route.ts" showLineNumbers
import { GoogleGenerativeAI } from '@google/generative-ai';
import { GoogleGenerativeAIStream, StreamingTextResponse } from 'ai';

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY || '');

export async function POST(req: Request) {
  // Extract the `prompt` from the body of the request
  const { prompt } = await req.json();

  // Ask Google Generative AI for a streaming completion given the prompt
  const response = await genAI
    .getGenerativeModel({ model: 'gemini-pro' })
    .generateContentStream({
      contents: [{ role: 'user', parts: [{ text: prompt }] }],
    });

  // Convert the response into a friendly text-stream
  const stream = GoogleGenerativeAIStream(response);

  // Respond with the stream
  return new StreamingTextResponse(stream);
}
```

### Wire up the UI

We can use the [`useCompletion`](/docs/reference/ai-sdk-ui/use-completion) hook to make it easy to wire up the UI. By default, the `useCompletion` hook will use the `POST` Route Handler we created above (it defaults to `/api/completion`). You can override this by passing a `api` prop to `useCompletion({ api: '...'})`.

```tsx file="app/page.tsx" showLineNumbers
'use client';

import { useCompletion } from '@ai-sdk/react';

export default function Completion() {
  const {
    completion,
    input,
    stop,
    isLoading,
    handleInputChange,
    handleSubmit,
  } = useCompletion();

  return (
    <div className="mx-auto w-full max-w-md py-24 flex flex-col stretch">
      <form onSubmit={handleSubmit}>
        <label>
          Say something...
          <input
            className="fixed w-full max-w-md bottom-0 border border-gray-300 rounded mb-8 shadow-xl p-2"
            value={input}
            onChange={handleInputChange}
          />
        </label>
        <output>Completion result: {completion}</output>
        <button type="button" onClick={stop}>
          Stop
        </button>
        <button disabled={isLoading} type="submit">
          Send
        </button>
      </form>
    </div>
  );
}
```

</Steps>

## Guide: Save to Database After Completion

It’s common to want to save the result of a completion to a database after streaming it back to the user. The `GoogleGenerativeAIStream` adapter accepts a couple of optional callbacks that can be used to do this.

```tsx file="app/api/completion/route.ts" showLineNumbers
export async function POST(req: Request) {
  // ...

  // Convert the response into a friendly text-stream
  const stream = GoogleGenerativeAIStream(response, {
    onStart: async () => {
      // This callback is called when the stream starts
      // You can use this to save the prompt to your database
      await savePromptToDatabase(prompt);
    },
    onToken: async (token: string) => {
      // This callback is called for each token in the stream
      // You can use this to debug the stream or save the tokens to your database
      console.log(token);
    },
    onCompletion: async (completion: string) => {
      // This callback is called when the completion is ready
      // You can use this to save the final completion to your database
      await saveCompletionToDatabase(completion);
    },
  });

  // Respond with the stream
  return new StreamingTextResponse(response);
}
```
